---
title: "Project 1: Exploring 100+ Years of US Baby Names"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    social: menu
    source: embed
---

```{r setup, include = FALSE}
library(tidyverse)
library(flexdashboard)
FILE_NAME <- here::here("data/names.csv.gz")
tbl_names <- readr::read_csv(FILE_NAME, show_col_types = FALSE)
knitr::opts_chunk$set(
  fig.path = "img/",
  fig.retina = 2,
  fig.width = 6,
  fig.asp = 9/16,
  fig.pos = "t",
  fig.align = "center",
  # dpi = if (knitr::is_latex_output()) 72 else 150,
  out.width = "100%",
  # dev = "svg",
  dev.args = list(png = list(type = "cairo-png")),
  optipng = "-o1 -quiet"
)
ggplot2::theme_set(ggplot2::theme_gray(base_size = 8))
```

### Header 1

```{r results = "hide"}
# PASTE BELOW >> CODE FROM question-1-transform
tbl_names_popular = tbl_names |> 
  # Keep ROWS for year > 2010 and <= 2020
  filter(year > 2010, year <= 2020) |> 
  # Group by sex and name
  group_by(sex, name) |> 
  # Summarize the number of births
  summarize(
    nb_births = sum(nb_births),
    .groups = "drop"
  ) |> 
  # Group by sex 
  group_by(sex) |>  
  # For each sex, keep the top 5 rows by number of births
  slice_max(nb_births, n = 5)

tbl_names_popular
```


```{r}
# PASTE BELOW >> CODE FROM question-1-plot BELOW
tbl_names_popular |> 
  # Reorder the names by number of births
  mutate(name = fct_reorder(name, nb_births)) |>
  # Initialize a ggplot for name vs. nb_births
  ggplot(aes(y = name, x = nb_births)) +
  # Add a column plot layer
  geom_col() +
  # Facet the plots by sex
  facet_wrap(~ sex, scales = "free_y") +
  # Add labels (title, subtitle, caption, x, y)
  labs(
    title = 'Number of babies per name',
    subtitle = 'Last decade, 2011-2020',
    caption = 'dont read this',
    y = 'Name',
    x = 'Number of Babies'
  ) +
  # Fix the x-axis scale 
  scale_x_continuous(
    labels = scales::unit_format(scale = 1e-3, unit = "K"),
    expand = c(0, 0),
  ) +
  # Move the plot title to top left
  theme(
    plot.title.position = 'plot'
  )
```

***

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### Header 2

```{r results = "hide"}
# PASTE BELOW >> CODE FROM question-2-transform 
tbl_names_popular_trendy = tbl_names |> 
  # Group by sex and name
  group_by(sex, name) |> 
  # Summarize total number of births and max births in a year
  summarize(
    nb_births_total = sum(nb_births),
    nb_births_max = max(nb_births),
    .groups = "drop"
  ) |> 
  # Filter for names with at least 10000 births
  filter(nb_births_total > 10000) |> 
  # Add a column for trendiness computed as ratio of max to total
  mutate(trend = nb_births_max / nb_births_total) |> 
  # Group by sex
  group_by(sex) |> 
  # Slice top 5 rows by trendiness for each group
  slice_max(trend, n = 5)

tbl_names_popular_trendy
```


```{r}
# PASTE BELOW >> CODE FROM question-2-visualize
plot_trends_in_name <- function(my_name) {
  tbl_names |> 
    # Filter for name = my_name
    filter(name == my_name) |> 
    # Initialize a ggplot of `nb_births` vs. `year` colored by `sex`
    ggplot(aes(x = year, y = nb_births, color = sex)) +
    # Add a line layer
    geom_line() +
    # Add labels (title, x, y)
    labs(
      title = glue::glue("Babies named {my_name} across the years!"),
      x = 'Year',
      y = 'Number of babies'
    ) +
    # Update plot theme
    theme(plot.title.position = "plot")
}
plot_trends_in_name("Steve")
plot_trends_in_name("Barbara")
```

***

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### Header 3

```{r results = "hide"}
# PASTE BELOW >> CODE FROM question-3-transform-1 and question-3-transform-2
tbl_names = tbl_names |> 
  # Add NEW column first_letter by extracting `first_letter` from name using `str_sub`
  mutate(first_letter = str_sub(name, 1, 1)) |>  
  # Add NEW column last_letter by extracting `last_letter` from name using `str_sub`
  mutate(last_letter = str_sub(name, -1, -1)) |> 
  # UPDATE column `last_letter` to upper case using `str_to_upper`
  mutate(last_letter = str_to_upper(last_letter))

tbl_names

tbl_names_by_letter = tbl_names |> 
  # Group by year, sex and first_letter
  group_by(year, sex, first_letter) |> 
  # Summarize total number of births, drop the grouping
  summarize(nb_births = sum(nb_births), .groups = "drop") |> 
  # Group by year and sex
  group_by(year, sex) |> 
  # Add NEW column pct_births by dividing nb_births by sum(nb_births)
  mutate(pct_births = nb_births / sum(nb_births))
  
tbl_names_by_letter
```


```{r}
# PASTE BELOW >> CODE FROM question-3-visualize-1
tbl_names_2020 <- tbl_names %>%
  filter(year == 2020)

ggplot(tbl_names_2020, aes(x = first_letter, y = nb_births)) +
  geom_col() +
  facet_wrap(~ sex, scales = "free_x") +
  labs(
    title = "Distribution of births by first letter for the year 2020, faceted by sex",
    x = "First Letter",
    y = "Number of Births"
  ) +
  theme(plot.title = element_text(hjust = 0.5))
```



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